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TLERAD: Transfer Learning for Enhanced Ransomware Attack Detection

Computers, Materials & Continua 2024年第11期81卷 2791-2818页

作者： Isha Sood Varsha sharm a School of Information Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India

Ransomware has emerged as a critical cybersecurity threat,characterized by its ability to encrypt user data or lock devices,demanding ransom for their *** ransomware detection methods face limitations due to their assumption of similar data distributions between training and testing phases,rendering them less effective against evolving ransomware *** paper introduces TLERAD(Transfer Learning for Enhanced Ransomware Attack Detection),a novel approach that leverages unsupervised transfer learning and co-clustering techniques to bridge the gap between source and target domains,enabling robust detection of both known and unknown ransomware *** proposed method achieves high detection accuracy,with an AUC of 0.98 for known ransomware and 0.93 for unknown ransomware,significantly outperforming baseline *** experiments demonstrate TLERAD’s effectiveness in real-world scenarios,highlighting its adapt-ability to the rapidly evolving ransomware *** paper also discusses future directions for enhancing TLERAD,including real-time adaptation,integration with lightweight and post-quantum cryptography,and the incorporation of explainable AI techniques.

关键词： Ransomware detection transfer learning unsupervised learning co-clustering cybersecurity machine learning lightweight cryptography post-quantum cryptography explainable AI TLERAD

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